Nanostring Analysis of Lactate Correlation Geneset
- Filtering low count reads
A. Determining Which Samples to Exclude
B. Determining Which Genes to Exclude
- Compare COV of HKGs to lactate cor. genes
A. Normalized nanostring data: comparing the COV for our 5 housekeeping
genes to the COV of the genes that remained after excluding false
positives
B. RNAseq CPM data: comparing the COV for our 5 housekeeping genes to
the COV of the genes that remained after excluding false positives
- Correlation of detected gene expression levels with 3-hour lactate
levels
A. Positively correlating genes
B. Negatively correlating genes
- Correlation figures for probes that meet all of the threshold
criteria
A. Heatmap showing relative expression of the gene set detected by
nanostring
B. Plot showing showing lactate levels for each sample (red) with
relative expression (grey) and mean relative expression (black) of the
gene set detected by nanostring. error bars are the variance of the mean
relative expression data.
C. Mean expression between Adequate and Low performing livers for the 7
lactate correlation genes.
Summary
Of the 10 samples used, one was not usable (LV13) due to an error in
sample prep. Another sample (LN11) was analyzed in triplicate to
demonstrate the low level of variation between technical replicates.
Data was normalized using internal positive controls and calculated in
the nSolver software provided by nanostring. Housekeeping genes from the
RNAseq data maintained a lower coefficient of variation then the 23
lactate correlation genes assessed. Of these 23 lactate correlation
genes, only 12 yielded sufficient data above the false discovery
threshold calculated using internal negative controls. Of these 12
genes, only 7 maintained a correlation of .8 with lactate levels as was
observed in the RNAseq experiment.
Filtering low count reads
Excluding any probes that did not exceed the maximum count observed
in the negative controls for that sample. This is the most stringent
false discovery identification method suggested by nanostring
A) Determining Which Samples to Exclude

- Excluding LV13 since none of the reads were greater than the
negative control threshold
B. Determining Which Genes to Exclude

- Excluding all genes with more than 2 false positives
Compare COV of HKGs to lactate cor. genes
Housekeeping genes should have generally lower coefficients of
variation compared to the lactate correlation genes
A. Normalized nanostring data: comparing the COV for our 5
housekeeping genes to the COV of the genes that remained after excluding
false positives

B. RNAseq CPM data: comparing the COV for our 5 housekeeping
genes to the COV of the genes that remained after excluding false
positives

Correlation of detected gene expression levels with 3-hour lactate
levels
Excluding any gene that does not have a correlation of at least .8
between the nanostring data and 3-hour lactate levels
A. Positively correlating genes
Note: GSTA1 is only able to meet the cutoff when rounded. (r=
0.7977059)
B. Negatively correlating genes
There are 7 genes that meet the correlation cutoff of .8 that
was previously used with the RNAseq data results. All of these genes
positively correlated with lactate, with no negatively correlating genes
having sufficient levels of correlation. Alternatively, setting the
cutoff more leniently to .65 would still only yield a total of 10 genes
which would include a single negatively correlating gene